The reconstruction of three-dimensional image data sets from two-dimensional projection images is already widely known from prior art. Computed tomography facilities (CT facilities) are mostly used to produce such three-dimensional image data sets in the x-ray imaging region, which are designed to be dedicated to the reconstruction of higher dimensional image data sets. Since that time, however, it has also been suggested to use other x-ray facilities, which can receive projection images by various projection directions in order to construct three-dimensional image data sets. A known example of this are C-arm x-ray facilities, which have a C-arm, on which an x-ray emitter and an x-ray detector, which lie opposite one another, are arranged. The receiving arrangement formed in this way can, for example, be rotated around the patient, in order to be able to receive the projection images and reconstruct a three-dimensional image data set therefrom.
As a result of the receiving and reconstruction of three-dimensional image data sets, artifacts often remain in the image data. Many algorithms are known for the elimination thereof, which, however, are not always able to completely eliminate the artifacts. Particularly problematic herein in the location chamber are low-frequency homogeneity artifacts, i.e. effects, which cause the same materials or generally the same material classes to not be imaged evenly in all locations of the image data set, and therefore to receive the same attenuation value.
It has already been indicated at this point, that the attenuation values in the region of the x-ray imaging are often specified as so called HU-values (Hounsfield units), which, however, already begin in the negative region, for example at −1000. Therefore, it is well-established to add an offset to the attenuation values in HU, for example of 1024, in order to receive principally positive image data, which can, if necessary, be more easily processed. Nevertheless, a conversion to HU is, of course, always possible without any problems.
In the case of the HU it is known that there exists a correlation with the attenuation coefficients of the tissue that is being considered, as HU are defined ultimately by the deviation from the attenuation coefficient for water, therefore set an attenuation value for water of 0 HU. Due to the described effects it can occur that despite the same present material class, and therefore attenuation coefficients lying in the same region, various attenuation values are present as an image datum in various regions of the three-dimensional image data set, in such a way that there is no homogeneity. Such homogeneity artifacts mainly pose a problem if low-contrast details should be identified in an image data set, for example a hemorrhage, a tumor or an infarct region.
Various homogeneity artifacts are known. Firstly, the so-called cupping artifacts exist, which mainly stem from scattered radiation. This causes the image data of the even material class at the edge of the image to become higher or lower, such that it results in a type of “bowl shape”. Capping artifacts are also known, which can result from irradiation that is too high or an increase of density value because of increasing beam hardening. Such homogeneity artifacts are particularly noticeable when the human head is being recorded, as mainly soft tissue is present in the inner chamber of the head, which can be understood as an attenuation class with extremely similar attenuation values. If, for example, an aneurysm should be detected, small differences in contrast are to be calculated. Problems also often occur in the case of such head recordings with regard to cupping artifacts, after the image data can increase to the back part of the skull, after increased beam hardening has occurred through the thicker part of the cranium (calotte).
These physical effects can, as has already been explained, be partially eliminated through software corrections, which is part of the pre-processing of the measured projection images, before the projection images are used as input data for the reconstruction algorithm. These software corrections, for example a scattering correction or a beam hardening correction, clearly increase the quality of the three-dimensional image data set; however, despite the use of these algorithms, there still remains a recognizable mass of inhomogeneity in the reconstructed three-dimensional image data set.